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Table 2 Performances of predictive models in terms of treatment responses in patients with DME

From: Machine learning and optical coherence tomography-derived radiomics analysis to predict persistent diabetic macular edema in patients undergoing anti-VEGF intravitreal therapy

  

Training set

  

Test set

 

Logistic

SVM

BPNN

Logistic

SVM

BPNN

SEN

0.904

0.923

0.962

0.783

0.826

0.913

SPE

0.741

0.667

0.926

0.727

0.636

0.636

ACC

0.848

0.835

0.949

0.765

0.765

0.824

PPV

0.870

0.842

0.962

0.857

0.826

0.840

NPV

0.800

0.818

0.926

0.615

0.636

0.778

F1

0.887

0.881

0.962

0.818

0.826

0.875

AUC

0.910

0.897

0.982

0.905

0.885

0.929

  1. SEN, sensitivity; SPE, specificity. ACC, accuracy; PPV, positive predictive value; NPV, negative predictive value; F1, F1 score; AUC, area under the receiver operating curve
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Journal of Translational Medicine

ISSN: 1479-5876

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